K nearest Neighbour Skill
The k-Nearest Neighbors (k-NN) algorithm is a simple and intuitive machine learning approach used for both classification and regression tasks. It operates on the principle of proximity, where a data point's class or value is determined by the majority (for classification) or average (for regression) of its k-nearest neighbors in the feature space.In k-NN, the choice of k, the number of neighbors considered, is a crucial parameter that influences the model's performance. A smaller k value makes the model more sensitive to local variations, while a larger k value smoothens the decision boundaries and reduces sensitivity to noise.The algorithm assumes that similar data points in the feature space tend to have similar outcomes. During prediction, the distance metric (commonly Euclidean distance) is used to measure proximity, and the majority or average label of the k-nearest neighbors determines the classification or regression result for the new data point.k-NN is straightforward to implement and suitable for datasets with complex decision boundaries. However, it may struggle with high-dimensional data and can be computationally expensive during prediction for large datasets. Despite these considerations, k-NN serves as a foundational algorithm in machine learning, aiding in understanding the basics of classification and regression tasks.
K nearest Neighbour Sub Skills
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